Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information
Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information
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发表时间：
2008-05
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通讯作者：
Kugatsu Sadamitsu;S. Sekine;Mikio Yamamoto
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作者：
Kugatsu Sadamitsu;S. Sekine;Mikio Yamamoto
本文提出了一种利用句间结构进行情感分析的新方法，特别是针对词语极性反转现象，如引用对方的观点。我们使用隐藏条件随机场（HCRF）的三种功能：过渡功能，极性功能和反转（极性）功能，这些现象建模。极性特征和反转特征被双重地添加到每个词，并且特征的每个权重由正语料库和负语料库的共同结构训练，例如，假设反转现象在两个极性语料库中由于相同的原因（特征）而发生。我们的方法取得了更好的精度比朴素贝叶斯方法和SVM一样好。
This paper proposes a new method of the sentiment analysis utilizing inter-sentence structures especially for coping with reversal phenomenon of word polarity such as quotation of others opinions on an opposite side. We model these phenomenon using Hidden Conditional Random Fields(HCRFs) with three kinds of features: transition features, polarity features and reversal (of polarity) features. Polarity features and reversal features are doubly added to each word, and each weight of the features are trained by the common structure of positive and negative corpus in, for example, assuming that reversal phenomenon occured for the same reason (features) in both polarity corpus. Our method achieved better accuracy than the Naive Bayes method and as good as SVMs.